Overview of the most interesting materials on data analysis and machine learning №6 (July 21 - 28, 2014)

In the next issue of the review of the most interesting materials on the topic of data analysis and machine learning, a number of articles are devoted to the practical application of various types of regression. There is an interesting series of articles on the use of machine learning in trading. As usual, a lot of materials are devoted to machine learning algorithms (including neural networks). There are several interesting video lectures, as well as many articles about the practical use of the R language in data analysis and machine learning.

Data analysis and machine learning materials

• 16 areas of analytics in comparison with Data Science
  An interesting comparison of the relatively new discipline of Data Science with various areas of analytics (data mining, machine leraning, statistics, etc.)
• Nonlinear regression in R
  4 types of nonlinear regression with examples in the R programming language.
• Logistic Regression Visualization with Shiny
  An article about creating interactive graphs for logistic regression visualization using the Shiny package for the R programming language.
• Everything you wanted to know about machine learning, but were afraid to ask (part 1)
  An interesting article on the basics of machine learning.
• Everything you wanted to know about machine learning, but were afraid to ask (part 2)
  Continuation of a series of articles devoted to the basics of machine learning.
• The difference between library () and require () in R
  A small article about when to use library () and require () in the R language. Often there is confusion in this issue.
• The use of machine learning for trading (part 1)
  Introduction to the topic of using machine learning for trading.
• The use of machine learning for trading (part 2)
  Continuing the theme of machine learning for trading.
• Applying linear regression with R
  Article on the use of 4 types of linear regression using the programming language R.
• Stanford University has published a large collection of data sets.
  Stanford University has published a large collection of graph data sets (Stanford Large Network Dataset Collection), that is, data that is organized in the form of graphs or networks. It seems that this is an excellent data set, where you can experiment and hone your skills in data analysis and machine learning.
• Weekly Digest from DataScienceCentral
  Regular weekly digest of articles on data analysis from the DataScienceCentral portal.
• Introduction to convolutional neural networks (part 1)
  Introductory article about currently popular convolutional neural networks, written in a fairly simple language.
• Introduction to convolutional neural networks (part 2)
  Continuation of the discussion about convolutional neural networks. In the second part, the author pays great attention to the theory of convolutional neural networks.
• Machine learning data sets
  List of resources where you can find a large number of interesting data sets (data sets) for machine learning and data analysis.
• The use of Markov chains in practice
  An example of a possible application of Markov chains in practical problems on the example of bank lending and risk assessment.
• Penalized regression with R
  An article about Penalized Regression using the R programming language.
• Search for duplicates using machine learning
  An interesting article, written in fairly simple language, about a typical problem of finding duplicates in records, using machine learning. The article contains examples of Python code.
• Simplify R code with the magrittr library and pipelines
  Simplifying code in the R programming language, using the magrittr library, which allows you to apply the pipeline approach to code.
• MLlib - a machine learning library for Apache Spark
  A short article about the MLlib machine learning library for the increasingly popular Apache Spark.
• Quoc Le Video Lectures by Deep Learning
  Quoc Le from the Google Brain team presents Deep Learning machine learning videos from the Machine Learning Summer School (MLSS '14) in Pittsburgh.
• 10 types of linear regression
  A small article about the question of the correct choice of various types of linear regression in machine learning.
• Applying Machine Learning for Kaggle Competitions
  To improve your machine learning skills, it is often important to study ready-made examples of solutions from data analysts. This article discusses an example of solving a known problem from the Kaggle website - the death of the passengers of the Titanic. The author of the solution uses the R programming language in his example.
• Introduction to machine learning
  Link to the second edition of a great book on machine learning.
• The Truth About Data Science Startups
  Interesting arguments about the problems that can be encountered when creating a startup in the field of Data Science.
• How to improve your machine learning skills
  A good short list of books on machine learning that will help you raise your level in this matter.
• Experiment in Yandex. How to identify a cracker using machine learning
  An article on the use of machine learning in Yandex to attempt to classify an attacker and a regular user according to behavioral signs. The details of the work of these algorithms, unfortunately, are not particularly disclosed.

Previous release: Review of the most interesting materials on data analysis and machine learning №5 (July 7 - 21, 2014)